03-07-2013, 02:30 PM
ENERGY-EFFICIENT BEACONLESS GEOGRAPHIC ROUTING IN WIRELESS SENSOR NETWORKS
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INTRODUCTION
A Mobile Ad Hoc Network (MANET) is a network consisting of a collection of nodes capable of communicating with each other without help from a network infrastructure.
Applications of MANETs include the battlefield applications, rescue work, as well as civilian applications like an outdoor meeting, or an ad-hoc classroom. With the increasing number of applications to harness the advantages of Ad Hoc Networks, more concerns arise for security issues in MANETs.
The absence of any fixed infrastructure, such as an array of base stations, makes adhoc networks radically different from other wireless LANs. Whereas communication from a mobile terminal in an “infrastructure” network, such as a cellular network, is always maintained with a fixed base-station, a mobile terminal (node) in an adhoc network can communicate directly with another node that is located within its radio transmission range. In order to transmit to a node that is located outside its radio range, data packets are relayed over a sequence of intermediate nodes using a store-and-forward “multihop” transmission principle. All nodes in an adhoc network are required to relay packets on behalf of other nodes. Hence, a mobile adhoc network is sometimes also called a multihop wireless network.
NEED FOR AD-HOC NETWORKS
Ad-Hoc networks are needed as mobile hosts need to communicate with each with no fixed infrastructure and no administrative help because
1. It may not be physically possible for the establishment of the infrastructure
2. It may not be practically economical to establish the infrastructure or
3. It may be because of the expediency of the situation does not permit the installation of the infrastructure.
Examples of the use of the MANET :
• Tactical operation – for fast establishment of military communication during the deployment of forces in unknown and hostile terrain.
• Rescue mission – for communication in areas without adequate wireless coverage.
• National security – for communication in times of national crisis, where the existing communication infrastructure is non-operational due to a natural disaster or a global war.
• Law enforcement – for fast establishment of communication in exhibitions, conferences, or sales presentations.
• Commercial use – for setting up communication in exhibitions, conferences, or sales presentations.
• Education – for operation of wall-free (virtual classrooms).
• Sensor networks - for communication between intelligent sensors mounted on mobile platforms.
EXISTING SYSTEM
In recent years mobile ad hoc networks (MANETs) have received tremendous attention because of their self-configuration and self-maintenance capabilities. While early research effort assumed a friendly and cooperative environment and focused on problems such as wireless channel access and multihop routing, security has become a primary concern in order to provide protected communication between nodes in a potentially hostile environment. Although security has long been an active research topic in wireline networks, the unique characteristics of MANETs present a new set of nontrivial challenges to security design. These challenges include open network architecture, shared wireless medium, stringent resource constraints [1], and highly dynamic network topology. Consequently, the existing security solutions for wired networks do not directly apply to the MANET domain.
CLASSIFICATION OF ROUTING PROTOCOLS IN MANET
A routing protocol is a protocol that specifies how routers communicate with each other, disseminating information that enables them to select routes between any two nodes on a computer network. Each router has a priori knowledge only of networks attached to it directly. A routing protocol shares this information first among immediate neighbors, and then throughout the network. This way, routers gain knowledge of the topology of the network.
Ad hoc On-demand Distance Vector (AODV):
The AODV routing protocol builds on top of the DSDV protocol that was previously described. AODV is an improvement of DSDV as it minimises the number of required broadcasts since it creates routes in an on-demand basis, in contrast to DSDV which maintains a complete set of routes [RT99]. It utilises destination sequence numbers to ensure loop-freedom at all times and to avoid the count-to-infinity problem associated with classical distance-vector protocols.
When a node needs a route to a destination it broadcasts a Route Request (RREQ) message. The RREQ message is spread throughout the network and as soon as the message reaches a node with a fresh enough route to the specific destination or the destination node itself, a Route Reply (RREP) message is unicasted back to the requesting node [PR03]. Generally AODV offers low overhead, quick adaptation to dynamic link conditions and low processing and memory overhead. Since the AODV routing protocol is the one that it used in this research and in the development of the Real-Time Intrusion Detection system it is presented in great detail in a following section.
SHARP
The Sharp Hybrid Adaptive Routing Protocol (SHARP) which automatically finds the balance point between proactive and reactive routing by adjusting the degree to which route information is propagated proactively versus the degree to which it needs to be discovered reactively. SHARP enables each node to use a different application-specific performance metric to control the adaptation of the routing layer. This paper describes application-specific protocols built on top of SHARP for minimizing packet overhead, bounding loss rate, and controlling jitter Routing Protocol (SHARP), which utilizes this fundamental trade-off between proactive versus reactive routing to find a good balance between route information propagated proactively and route information that is left up to on demand discovery. SHARP utilizes both a proactive and a reactive protocol to perform routing. Each SHARP node determines the network neighborhood, called proactive zone, in which routing information pertaining to itself is disseminated proactively. SHARP relies on a novel proactive routing algorithm that is both efficient and analytically tractable.
The effect of mobility-induced location errors on geographic routing
A neighbor location prediction scheme is introduced as a solution to the LLNK problem To avoid the bad next-hop node selection, which may result in LLNK problems, the current locations of neighbor nodes are estimated at the moment of packet routing decision with NLP. Estimates are based on the recent beacon information received from neighbor nodes. The neighbor list includes the following additional fields for neighbor location estimation: last beacon time (LBT), node speed in the direction of x-axis (Sx) and yaxis (Sy). When a node receives a new beacon from a neighbor, the current time is stored in LBT together with the location of the neighbor Each node knows (or estimates) its approximate radio range and does not forward a packet to a neighbor node that is currently located outside of its range based on the estimated position to avoid LLNK. With NLP, a packet is forwarded to a neighbor node that meets the following two conditions:
A neighbor node that has a closest distance to a destination node from the estimated location of a neighbor node - Distance to a neighbor node is less than the transmission range of a forwarding node. The neighbor list is reconstructed by incorporating the transmission range information and using the estimated neighbor location information attained from this simple calculation. The NLP technique is then used to blacklist neighbor nodes that are estimated to be out of the communication range at the moment of packet forwarding. a destination location prediction (DLP) scheme is proposed as a second part of MP. With DLP, each node searches its neighbor list for the destination node before it makes a packet forwarding decision based on the location information of the destination.
NODE LIFETIME PREDICTION ALGORITHM
Two nodes that have the same residual energy level, an active node that is used in many data-forwarding paths consumes energy more quickly, and thus, it has a shorter lifetime than the remaining inactive node. The node lifetime that is based on its current residual energy and its past activity solution that does not need to calculate the predicted node lifetime from each data packet. Used an exponentially weighted moving average method to estimate the energy drain rate evi . Ei represents the current residual energy of node i, and evi is the rate of energy depletion. Ei can simply be obtained online from a battery management instrument, and evi is the statistical value that is obtained from recent history. The estimated energy drain rate in the nth period is evin, and evi(n−1) is the estimated energy drain rate in the previous (n − 1)th period, α denotes the coefficient that reflects the relation between evin and evin−1, and it is a constant value with a range of [0, 1].